[Paper Review] Computational Methodology for the Prediction of Functional Requirement Variations Across the Product Life-Cycle

Guillaume Mandil, Alain Desrochers+1 more|ArXiv.org|May 6, 2009

Manufacturing Process and Optimization9 references3 citations

TL;DR

This paper presents a computational methodology to predict functional requirement variations—specifically clearance in mechanical assemblies—across a product's life cycle by modeling thermo-mechanical load effects on part dimensions. Using finite element analysis and simulation, it links machining-stage measurements to in-service functional performance, enabling accurate prediction of operational clearances without direct field measurements.

ABSTRACT

The great majority of engineered products are subject to thermo-mechanical loads which vary with the product environment during the various phases of its life-cycle (machining, assembly, intended service use...). Those load variations may result in different values of the parts nominal dimensions, which in turn generate corresponding variation of the effective clearance (functional requirement) in the assembly. Usually, and according to the contractual drawings, the parts are measured after the machining stage, whereas the interesting measurement values are the ones taken in service for they allow the prediction of clearance value under operating conditions. Unfortunately, measurement in operating conditions may not be practical to obtain. Hence, the main purpose of this research is to create, through computations and simulations, links between the values of the loads, dimensions and functional requirements during the successive phases of the life cycle of some given product. [...]

Motivation & Objective

• Address the challenge of predicting functional requirements (e.g., clearance) in mechanical assemblies under varying operational conditions.
• Overcome the limitation of relying solely on post-machining measurements, which do not reflect in-service performance.
• Establish computational links between loading conditions, part dimensions, and functional requirements across all life-cycle phases.
• Enable accurate prediction of effective clearance during service use through simulation-based extrapolation from manufacturing-stage data.
• Support life-cycle management in product design and tolerance analysis by integrating environmental and operational load effects.

Proposed method

• Model the product’s life cycle in distinct phases: machining, assembly, and service use.
• Apply thermo-mechanical loading conditions representative of each phase to simulate part deformation and dimensional changes.
• Use finite element analysis (FEA) to compute stress and displacement fields under varying loads.
• Correlate simulated part deformations with nominal dimensions to predict effective clearance in assemblies.
• Integrate material behavior and thermal expansion effects into the simulation framework to reflect real operating environments.
• Validate the methodology by comparing simulated clearances with expected functional performance criteria.

Experimental results

Research questions

• RQ1 How do thermo-mechanical loads during different life-cycle phases affect part dimensions and functional clearances?
• RQ2 To what extent can post-machining measurements be used to predict in-service functional performance?
• RQ3 Can computational simulation accurately predict functional requirement variations without direct in-service measurements?
• RQ4 What is the impact of environmental and operational loading on dimensional stability and clearance in mechanical assemblies?
• RQ5 How can a consistent computational framework link manufacturing-stage data to service-stage functional outcomes?

Key findings

• The methodology successfully predicts functional clearance variations across the product life cycle using only machining-stage measurements and simulation.
• Simulated clearances under operational loads closely match expected functional performance, validating the predictive accuracy of the model.
• Thermo-mechanical loading significantly alters part dimensions, leading to measurable changes in functional clearance even when nominal dimensions remain unchanged.
• The approach enables early identification of potential functional failures due to clearance deviations under real operating conditions.
• The integration of FEA with tolerance and geometric variation modeling allows for robust prediction of functional performance across life-cycle phases.
• The method supports life-cycle tolerance analysis by quantifying the influence of environmental and service loads on functional requirements.